10 Software Development Prospects in 2018

2018 will be a year of tensions and anxiety for developers. On the one hand, the expectations and pressure that new products and tools such as block chains, chatbots, serverless technologies, and machine learning will mature to the point where they can be put to practical use, but on the other hand, . However, there will be good news in this pressure. 

The year 2018 is expected to open up to developers with a tug of war between the hope of new opportunities and the pressure to do better quality work. Let's see how these conflicting forces work and how they will affect developers.

1. Commercialization of B2B transactions using block chains. 
Companies are increasingly interested in the strengths of security, reliability, and efficiency of block-chain-based transactions. This year, block-chain applications will emerge in various areas such as financial services and manufacturing supply chain. Block chaining is a technology that enables secure, efficient, and always reliable transactions without intermediaries between institutions that can not guarantee mutual reliability. 

Suppose a company orders a particular product from a foreign manufacturer. At this time, the ordered goods are transported through a separate transportation company, after passing through customs, and then arriving at the ordering company through the domestic delivery company. Today, e-mail and spreadsheets demonstrate and coordinate all these processes, and the people and processes required to do so are also significant. On the other hand, if the block chain agrees with the fact that the minimum number of persons concerned is 'proof of this transaction', the existing manual process and the adjustment process are omitted by updating the contents of the block chain to the permanent record in the block chain. 

The block-chain cloud service presents a new perspective on scalability, resilience and security, and ensures a high level of pre-built integration with enterprise systems. In addition, it will provide developers with an opportunity to move beyond their hyperlinked fabric implementations and focus more on business applications. 

2. Chatbot to actually communicate with customers and employees
As time goes on, people will not want to endure the inconvenience of using multiple apps separately to do the same thing. For example, if you need to use different apps for different airlines to check in flights and get tickets. One of the best ways to get rid of these inconveniences is to do this using the messaging features most used on smartphones. 

Messaging apps are attractive because they are immediate, expressive, and conversational. In addition, you do not need to be trained for use. Thanks to the development of artificial intelligence and natural language processing technology, we now have the ability to ask and answer questions to AI bots using voice assistant technologies such as Facebook Messenger, Slack, Wit Chat, Watts App, or Amazon Alexa or Google Home . 

As developers, we have been able to create intelligent bots cloud services in a short period of time to understand customer intentions, maintain conversations, and maintain integration with backend systems. For example, let's say you want to send a photo of an actor's clothes from a movie to a bot in a clothing store that you frequently go to. The bot will utilize image recognition technology and AI technology to recommend a similar style of clothing for the user.

The use of these bots is also good for workers. For example, you can use a bot when you ask for a few days to leave, ask for help at the help desk, or order a replacement laptop. Artificial intelligence bots will recommend the right notebook model for the employee and update the order status constantly. Developers are more likely to experiment with bots for the same company employee when they first build bots. Even if you fail, it will be much less hurtful if you fail in an experiment with other employees in your company. 

3. Buttonless world: AI interfacing with
AI AI is becoming a new standard in UI. With the advent of this new interface, the existing concurrent request-response model of using apps and services will gradually disappear. Of course, realistic smartphones are still 'low-IQ' tools that users have to pick up, run specific apps, and respond to commands. On the other hand, the next generation of intelligent apps can initiate their own interaction with users via push notifications. And one step further, you'll be able to experience artificial intelligence apps, bots, and virtual assistants looking for themselves in a variety of contexts and contexts. Here are two examples. 

- Apps that analyze users' spending report approval patterns and automatically make expenditure resolution decisions reach 99% self-approval rating. The app minimizes the risk of errors by requiring manual approval of exceptional reports that require user review.

- Analytic apps are provided to analyze analysts' existing analytics data, business analysts' question history, and other analysts' queries. As the size of the data that an organization collects increases, the level of questions that the AI ​​makes about that data can also improve. 

Developers can now identify and experiment with what data is needed for the business app they want to develop, what perspective they will derive from the transactions they make, and the most valid business decisions based on information derived from these predictive AIs. . Built-in AI automates many of the tasks that are currently being done manually, predicting what the user needs and delivering the right functionality at the right time with the appropriate means.